The potential for CO \u3c inf\u3e 2 -induced acidification in freshwater: A great lakes case study

Ocean acidification will likely result in a drop of 0.3–0.4 pH units in the surface ocean by 2100, assuming anthropogenic CO2 emissions continue at the current rate. Impacts of increasing atmospheric pCO2 on pH in freshwater systems have scarcely been addressed. In this study, the Laurentian Great Lakes are used as a case study for the potential for CO2-induced acidification in freshwater systems as well as for assessment of the ability of current water quality monitoring to detect pH trends. If increasing atmospheric pCO2 is the only forcing, pH will decline in the Laurentian Great Lakes at the same rate and magnitude as the surface ocean through 2100. High-resolution numerical models and one high-resolution time series of data illustrate that the pH of the Great Lakes has significant spatio-temporal variability. Because of this variability, data from existing monitoring systems are insufficient to accurately resolve annual mean trends. Significant measurement uncertainty also impedes the ability to assess trends. To elucidate the effects of increasing atmospheric CO2 in the Great Lakes requires pH monitoring by collecting more accurate measurements with greater spatial and temporal coverage

Explicit physical knowledge in machine learning for ocean carbon flux reconstruction: The pCO2-Residual Method

he ocean reduces human impacts on global climate by absorbing and sequestering CO2 from the atmosphere. To quantify global, time-resolved air-sea CO2 fluxes, surface ocean pCO2 is needed. A common approach for estimating full-coverage pCO2 is to train a machine learning algorithm on sparse in situ pCO2 data and associated physical and biogeochemical observations. Though these associated variables have understood relationships to pCO2, it is often unclear how they drive pCO2 outputs. Here, we make two advances that enhance connections between physical understanding and reconstructed pCO2. First, we apply pre-processing to the pCO2 data to remove the direct effect of temperature. This enhances the biogeochemical/physical component of pCO2 in the target variable and reduces the complexity that the machine learning must disentangle. Second, we demonstrate that the resulting algorithm has physically understandable connections between input data and the output biogeochemical/physical component of pCO2. The final pCO2 reconstruction agrees modestly better with independent data than most other approaches. Uncertainties in the reconstructed pCO2 and impacts on the estimated CO2 fluxes are quantified. Uncertainty in piston velocity drives substantial flux uncertainties in some regions, but does not increase globally-integrated estimates of uncertainy in CO2 fluxes from observation-based products. Our reconstructed CO2 fluxes show larger interannual variability than smoother neural network approaches, but a lesser trend since 2005. We estimate an air-sea flux of -1.8 Pg C / yr (anthropogenic flux of -2.3 ± 0.5 PgC/yr) for 1990-2019, agreeing with other data products and the Global Carbon Budget 2020 (-2.3 ± 0.4 PgC/yr). Key Points A new approach for pCO2 reconstruction applies pre-processing to remove the direct effect of temperature, simplifying the target variable for machine learning Reconstructed pCO2 captures independent data more closely than most existing products Estimated ocean carbon uptake has a trend since 2005 (-0.05 Pg C / yr2) that is on the lower end of previous observation-based estimates Plain Language Summary The ocean absorbs carbon dioxide from the atmosphere, moderating the human impact on Earth’s climate. To quantify how much carbon dioxide is removed from the atmosphere each year, we must know how much gas is exchanged at each location across the ocean over time. The observations necessary to quantify this gas exchange are very sparse and require gap-filling in both space and time. Because of the heterogeneity of this gas exchange, complex relationships between the ocean observations with near global coverage and ocean carbon are determined using machine learning algorithms and other statistical techniques. A concern is that these statistical algorithms do not require inputs to be linked to outputs in a manner consistent with ocean carbon cycle process understanding. Here, we develop a novel machine learning approach that starts by removing known physical signals from the data to create a cleaner signal for the computer algorithm to learn. Additional analysis demonstrates appropriate mechanistic links between algorithm inputs and outputs

Variability in the Global Ocean Carbon Sink From 1959 to 2020 by Correcting Models With Observations

The ocean reduces human impact on the climate by absorbing and sequestering CO2. From 1950s to the 1980s, observations of pCO(2) and related ocean carbon variables were sparse and uncertain. Thus, global ocean biogeochemical models (GOBMs) have been the basis for quantifying the ocean carbon sink. The LDEO-Hybrid Physics Data product (LDEO-HPD) interpolates sparse surface ocean pCO(2) data to global coverage by using GOBMs as priors, and applying machine learning to estimate full-coverage corrections. The largest component of the GOBM corrections are climatological. This is consistent with recent findings of large seasonal discrepancies in GOBMs, but contrasts the long-held view that interannual variability is a major source of GOBM error. This supports extension of the LDEO-HPD pCO(2) product back to 1959, using a climatology of model-observation misfits prior to 1982. Consistent with previous studies for 1980 onward, air-sea CO2 fluxes for 1959-2020 demonstrate response to atmospheric pCO(2) growth and volcanic eruptions

Climate change expands the spatial extent and duration of preferred thermal habitat for lake Superior fishes.

Climate change is expected to alter species distributions and habitat suitability across the globe. Understanding these shifting distributions is critical for adaptive resource management. The role of temperature in fish habitat and energetics is well established and can be used to evaluate climate change effects on habitat distributions and food web interactions. Lake Superior water temperatures are rising rapidly in response to climate change and this is likely influencing species distributions and interactions. We use a three-dimensional hydrodynamic model that captures temperature changes in Lake Superior over the last 3 decades to investigate shifts in habitat size and duration of preferred temperatures for four different fishes. We evaluated habitat changes in two native lake trout (Salvelinus namaycush) ecotypes, siscowet and lean lake trout, Chinook salmon (Oncorhynchus tshawytscha), and walleye (Sander vitreus). Between 1979 and 2006, days with available preferred thermal habitat increased at a mean rate of 6, 7, and 5 days per decade for lean lake trout, Chinook salmon, and walleye, respectively. Siscowet lake trout lost 3 days per decade. Consequently, preferred habitat spatial extents increased at a rate of 579, 495 and 419 km(2) per year for the lean lake trout, Chinook salmon, and walleye while siscowet lost 161 km(2) per year during the modeled period. Habitat increases could lead to increased growth and production for three of the four fishes. Consequently, greater habitat overlap may intensify interguild competition and food web interactions. Loss of cold-water habitat for siscowet, having the coldest thermal preference, could forecast potential changes from continued warming. Additionally, continued warming may render more suitable conditions for some invasive species

Modern air-sea flux distributions reduce uncertainty in the future ocean carbon sink

The ocean has absorbed about 25% of the carbon emitted by humans to date. To better predict how much climate will change, it is critical to understand how this ocean carbon sink will respond to future emissions. Here, we examine the ocean carbon sink response to low emission (SSP1-1.9, SSP1-2.6), intermediate emission (SSP2-4.5, SSP5-3.4-OS), and high emission (SSP5-8.5) scenarios in CMIP6 Earth System Models and in MAGICC7, a reduced-complexity climate carbon system model. From 2020–2100, the trajectory of the global-mean sink approximately parallels the trajectory of anthropogenic emissions. With increasing cumulative emissions during this century (SSP5-8.5 and SSP2-4.5), the cumulative ocean carbon sink absorbs 20%–30% of cumulative emissions since 2015. In scenarios where emissions decline, the ocean absorbs an increasingly large proportion of emissions (up to 120% of cumulative emissions since 2015). Despite similar responses in all models, there remains substantial quantitative spread in estimates of the cumulative sink through 2100 within each scenario, up to 50 PgC in CMIP6 and 120 PgC in the MAGICC7 ensemble. We demonstrate that for all but SSP1-2.6, approximately half of this future spread can be eliminated if model results are adjusted to agree with modern observation-based estimates. Considering the spatial distribution of air-sea CO _2 fluxes in CMIP6, we find significant zonal-mean divergence from the suite of newly-available observation-based constraints. We conclude that a significant portion of future ocean carbon sink uncertainty is attributable to modern-day errors in the mean state of air-sea CO _2 fluxes, which in turn are associated with model representations of ocean physics and biogeochemistry. Bringing models into agreement with modern observation-based estimates at regional to global scales can substantially reduce uncertainty in future role of the ocean in absorbing anthropogenic CO _2 from the atmosphere and mitigating climate change

Can spatial heterogeneity explain the perceived imbalance in Lake Superior\u27s carbon budget? A model study

Lake Superior is the largest lake in the world by surface area, containing 10% of the world\u27s surface freshwater. Yet, little is known about its role within the regional carbon budget. Observational studies on Lake Superior have been limited by harsh winters and the challenges of covering such a vast expanse. To date, carbon budgets extrapolated from observational studies are largely out of balance and suggest a large efflux of carbon dioxide to the atmosphere (∼3 TgC/yr) that cannot be supported by the estimated net inputs into the lake ( \u3c 1 TgC/yr). We couple a hydrodynamic model of Lake Superior to an ecosystem model to understand the seasonal cycle of the partial pressure of carbon dioxide (pCO \u3c inf\u3e 2 ) in the lake surface waters, the resulting air-lake carbon dioxide (CO \u3c inf\u3e 2 ) fluxes, and whether spatial heterogeneity can explain the previously imbalanced carbon budget. The model sufficiently simulates lake productivity, circulation, respiration, pCO \u3c inf\u3e 2 , and chlorophyll. We find that the seasonal cycle of pCO \u3c inf\u3e 2 is generally a double sinusoidal curve during the simulated period of 1996-2001. The lake acts as a sink of carbon dioxide in summer and during late winter of cold years and as a source to the atmosphere during winter and spring. We find significant spatial heterogeneity of respiration in Lake Superior, with near-shore to offshore rates of respiration varying by two orders of magnitude. Thus, Lake Superior need not act as a significant source of carbon dioxide (∼0.5 TgC/yr) to the atmosphere in order to be consistent with in situ observations of respiration. © 2012. American Geophysical Union. All Rights Reserved

Spatially explicit trends in the duration of preferred thermal habitat.

<p>Trends in the number of days (days•decade⁻¹) when preferred temperatures are present between 1979–2006 for all modeled lake points. Trends were computed using ordinary least squares regression using α = 0.10 (see methods). White indicates no significant trend in growing days.</p

Spatial extent and duration of preferred thermal habitat in contrasting years.

<p>Days with available preferred temperature are shown for walleye, Chinook salmon, lean lake trout, and siscowet trout in 1979 and 2006 at all modeled lake points. Preferred temperatures for walleye, Chinook salmon, lean lake trout, and siscowet trout are 21(±2°C), 13(±2°C), 10 (±2°C), and 4 (±2°C), respectively.</p

Interannual variability in preferred thermal habitat.

<p>Variability is shown as the standard deviation in days with available preferred habitat across all years from 1979 to 2006.</p

Observed variability of Lake Superior pCO \u3c inf\u3e 2

We present and compare direct and indirect pCO2 observations taken in Lake Superior in the last decade and use them to understand temporal and spatial variability in lake carbon cycle processes. In situ observations from 2001 and biannual survey data for 1996-2006 indicate that Lake Superior was, on average, supersaturated (annual mean = 46.7 ± 17.3 Pa [461 ± 171 μatm]) with respect to atmospheric pCO2 (mean 5 38.3 6 0.6 Pa) in April and close to equilibrium (mean = 37.5 ± 6.7 Pa) with respect to atmospheric pCO2 (mean = 36.4 ± 0.7 Pa) in August. Both data sets indicate that temporal variability in surface lake pCO2 from weekly to interannual timescales was predominantly controlled by changing dissolved inorganic carbon and associated changes in pH. An unstratified water column appears to have limited pCO2 fluctuations in spring. Through summer and into early fall, pCO2 variability on a daily timescale at 12 m increased with time to a maximum amplitude of 19 Pa, likely as a result of internal waves on the thermocline. Year-to-year changes in mean surface lake pCO2 and temperature were of the same sign and approximate magnitude at all observed points, consistent with the lake\u27s small size relative to the synoptic-scale meteorological systems that force it. Variability in pCO2 was not correlated with major climate indices. While these data provide a first large-scale overview of Lake Superior\u27s pCO2 and its temporal variability, their time-space resolution and accuracy are not sufficient to further refine previously imbalanced lake-wide carbon budgets